Jacqueline Chuah received her B.Sc (Hons) in Biological Sciences from the Nanyang Technological University, Singapore. In 2013 she joined Dr. Daniele Zink’s team at the Institute of Bioengineering and Nanotechnology of the Agency for Science, Technology and Research, Singapore. Dr. Zink’s lab develops organ-specific predictive screening technologies for in vitro toxicology/ nanotoxicology and conducts stem cell research with an emphasis on applications in predictive toxicology. Jacqueline’s work is focused on developing stem cell-based assays to predict kidney toxicity.

The kidney is a major target for compound-induced toxicity. Animal-free
alternative methods are required by governmental agencies and various
industries to decrease costs and increase the throughput of
nephrotoxicity prediction. We have developed the first accurate and
pre-validated in vitro models for predicting compound-induced
nephrotoxicity in humans (Loo and Zink, 2017; Chuah and Zink, 2017;
Kandasamy et al., 2015; Li et al., 2014; Li et al., 2013; Su et al.,
2014; Su et al., 2016). Our models include the first and only
pre-validated and predictive stem cell-based renal in vitro models (Li
et al., 2014; Kandasamy et al., 2015; Chuah and Zink, 2017). The most
advanced of these models is based on induced pluripotent stem cell
(iPS)-derived renal proximal tubular (PTC)-like cells. A rapid one-step
protocol has been established for the generation of PTC-like cells in 8
days of differentiation, and these cells can be directly used for
compound screening (Kandasamy et al., 2015). Alternatively, PTC-like
cells can be applied after cryopreservation. By combining iPS-derived
renal cell-based assays with machine learning methods, a test balanced
accuracy of 87% could be achieved with respect to nephrotoxicity
prediction (Kandasamy et al., 2015; Chuah and Zink, 2017). In addition,
underlying mechanisms of drug-induced cellular injury could be correctly
identified. We have also established a high-content screening (HCS)
platform that combines high-content imaging of renal cells with
automated phenotypic profiling and machine learning methods (Su et al.,
2016). The automated HCS platform has a test balanced accuracy ranging
between 82%-89%, depending on the human renal cell type used (Su et al.,
2016; Loo and Zink, 2017). Based on these technologies we are currently
developing a portfolio of platforms for the prediction of
compound-induced toxicity to various organs. In addition, a
kidney-on-chip platform for repeated dose testing is under development.
This platform appears to be suitable for the assessment of the human
dose response.